Quantitative analysis method of medical streamlines outside hospital space based on depth image and point cloud data

Abstract

The present invention is applicable to the technical field of current status assessment and optimal design of traffic flow lines in the outdoor space of a building complex, and provides a quantitative analysis method for medical flow lines in the outdoor space of a hospital based on depth images and point cloud data. This method uses the external space status of the research site and patient-related data to conduct actual measurements of the congestion level, crowd movement speed information, crowd behavior activities, etc. of the established research site with a functional orientation, providing a more scientific and reasonable basis for the optimization and transformation of the site's traffic organization. This method proposes a method for quantitatively analyzing the medical efficiency of the space flow lines outside the hospital, and provides the best medical navigation route for in-hospital patients, laying a quantitative data foundation for the precise optimization of the traffic organization outside the hospital. A quantitative analysis and optimization method for traffic organization in the outdoor environment of the hospital is proposed for the research site, which is beneficial to promote the cross-development of planning and management of large and medium-sized hospitals in the city, and helps related site research to effectively improve medical efficiency and achieve the purpose of improving hospital service quality.

Description

Technical Field

[0001] The present invention belongs to the technical field of current status assessment and optimization design of traffic flow lines in outdoor spaces of building complexes, and in particular relates to a quantitative analysis method of medical flow lines in outdoor spaces of hospitals based on depth images and point cloud data. Background Art

[0002] Large hospitals are large and complex, and their spatial layout, environmental factors, and traffic flow all impact patient wait times, treatment efficiency, and overall patient experience. The depth images and point cloud data generated by the ZED 2i depth camera can capture the three-dimensional geometry of objects, enabling object detection, human tracking, spatial mapping, and continuous scanning of the surrounding environment to create 3D maps.

[0003] Currently, depth cameras are primarily used in fields such as machinery, computers, and agriculture. Research on the environment outside hospitals has largely focused on traditional qualitative analysis, lacking the ability to quantitatively analyze the flow of medical personnel outside hospitals using deep image point cloud data analysis methods. This would allow for more accurate and scientific quantitative data to more efficiently identify key issues and address these issues through optimization of hospital access routes, distribution centers, and other areas. Summary of the Invention

[0004] The purpose of the present invention is to provide a quantitative analysis method for medical streamlines outside hospital space based on depth images and point cloud data, aiming to solve the technical problems existing in the existing technology identified in the background technology.

[0005] The present invention is achieved by providing a quantitative analysis method for medical streamlines outside a hospital based on depth images and point cloud data, the method comprising:

[0006] Step 1: Set up the ZED 2i camera operating environment and test it on the terminal to confirm the environment configuration status;

[0007] Step 2: Obtain real data on outpatient and inpatient visits, and collect and process hospital status data. Through quantitative data analysis and qualitative status research, obtain patient flow at different time periods, the correlation between patient flow and time, and patients' high-frequency medical treatment paths and gathering spaces;

[0008] Step 3: Use the ZED 2i depth camera to detect people in high-frequency medical gathering spaces, obtain the three-dimensional spatial coordinates of each target object in each gathering space, and use the camera to perform plane detection on the space to obtain a grid plane map with coordinate values. Analyze the probability density distribution of people's activities at different locations in the space to represent the utilization rate of different locations in the space. Calculate the area occupied by pedestrians and use different thresholds to represent the congestion status of the gathering space.

[0009] Step 4: Use the ZED 2i depth camera to track people in high-frequency medical gathering spaces, obtain 18 skeletal points, construct a spatiotemporal graph, and pass it to the ST-GCN model. The ST-GCN model extracts the spatiotemporal features of the skeletal sequence, determines the action category, and calculates the proportion of action types in the space.

[0010] Step 5: Use road static information to divide high-frequency medical roads and select static nodes, which include traffic capacity nodes, road condition nodes, and roadside information nodes;

[0011] Step 6: Use the ZED 2i depth camera to detect people on frequently visited medical routes, obtaining information on the movement speed of each person as they pass through each segment of the route. Quantitative data is combined with different thresholds to characterize the congestion status of each road segment. The intersections of sections with different congestion levels are defined as dynamic nodes, which are then integrated with static nodes to form different road units.

[0012] Step 7: Analyze the current status of the qualitative research and quantitative data of high-frequency medical gathering spaces and high-frequency medical roads, derive corresponding strategies for optimizing medical flow lines outside the hospital, and use intelligent algorithms to calculate medical navigation routes.

[0013] As a further solution of the present invention, step 2 specifically includes:

[0014] Step 2.1: Analyze the current situation of the hospital under study, analyze the traffic flow organization and medical environment, and conduct a qualitative analysis of the current problems;

[0015] Step 2.2: Divide the acquired data of tens of thousands of patients into different time units, and retrieve patient data in different months, weekdays and holidays, and different time periods of the day.

[0016] The correlation between patient flow and different time units is calculated using the corresponding patient flow and time values ​​and their average values, and used as the basis for setting the shooting period. The correlation calculation formula is:

[0017]

[0018] Among them, r is the correlation, f i and t i are the quantitative values ​​of patient flow and time, respectively, and is the average value of patient flow and time;

[0019] Step 2.3: Divide the patient data into different treatment departments and inpatient wards. Based on the hospital's department and inpatient building layout, replace the scattered departments and wards in the patient data with different numbers;

[0020] Step 2.4: Use the numbering frequency to determine the spaces and roads where patients frequently seek medical treatment within the hospital, and determine them as the points and routes for photography and data analysis.

[0021] As a further solution of the present invention, step 3 specifically includes:

[0022] Step 3.1: Using the ZED 2i depth camera's object detection function, based on the crowd-time correlation analysis results, select a specific date and several fixed time points as the starting point. Record a fixed-length recording of each gathering space, set people as detection targets, activate the location tracking module, and track the crowd.

[0023] Step 3.2: Store and traverse the list of human object spatial coordinates to obtain continuous three-dimensional spatial coordinate points of all human objects, remove object data with a confidence level less than 10%, and retain the frame-by-frame continuous plane spatial coordinates (x, z) of the remaining objects;

[0024] Step 3.3: Read the plane coordinate data of the human object, use kernel density estimation to draw a density scatter plot, and use the ZED 2i depth camera to detect the plane of the gathering space. Enable position tracking, obtain the plane data, and convert it into a grid to obtain the plane information of the gathering space. The kernel density estimation formula is:

[0025]

[0026] in, is the estimated value of the unknown density function f at point x; n is the number of samples; x i is the observed value of the i-th sample; K is the kernel function, a non-decreasing symmetric distribution; h is the smoothing parameter used to control the smoothness of the estimate;

[0027] Step 3.4: Unify the coordinate systems of the gathering space plan and the scatter density map and overlay them to obtain the crowd activity density distribution of the gathering space. Use the crowd activity density distribution map to represent the utilization rate of different locations in the gathering space.

[0028] Step 3.5: Calculate the pedestrian occupied area of ​​the gathering space based on the planar width of the space and the peak pedestrian flow obtained from target detection. Adjust the appropriateness based on the structural characteristics of the special population in the hospital. Calculate the congestion threshold based on the proportion of patients in wheelchairs and the proportion of patients requiring bed propulsion. Use the k-means algorithm to iterate multiple sets of calculated pedestrian occupied area values ​​to find cluster centers and determine the congestion status of the gathering space. The pedestrian occupied area calculation formula is:

[0029]

[0030] Where S is the area occupied by pedestrians, in m 2 / person; M is the effective area of ​​the gathering space, unit is m 2 ; Q is the passenger flow during peak hours, the unit is people;

[0031] The calculation formula for the congestion threshold is:

[0032] θ=1.04k1+10k2+(1-k1-k2)C;

[0033] Where θ is the threshold number, the unit is m 2 / person; k1 is the proportion of patients in wheelchairs in the hospital; k2 is the proportion of patients who need beds to move forward in the hospital; C is the threshold for evaluating the degree of crowding in general space, unit is m 2 / person; 1.04 and 10 are the general floor space values ​​for patients in wheelchairs and patients who need to be pushed forward by beds, respectively, in m 2 / people.

[0034] As a further solution of the present invention, step 4 specifically includes:

[0035] Step 4.1: Using the ZED 2i Depth Camera SDK's depth module and position tracking module, select a date and several fixed time points as starting points, perform fixed-point recording of each gathering space for a fixed duration, extract the 3D positions of the skeleton and key points of each object in the video, remove data with a confidence level less than 10%, and save the 2D coordinate data of the human skeleton nodes in each frame.

[0036] Step 4.2: Based on the human joint connection information, the joint coordinates of each frame are constructed into a space-time graph G = (V, E), where V represents the set of joint points and E represents the set of edges. In the space-time graph, edges include spatial edges and temporal edges. Spatial edges represent natural connections between joints, and temporal edges represent connections between the same joints in consecutive frames.

[0037] Step 4.3: Pass the human joint coordinates as input to the ST-GCN model. Use multiple graph convolution modules to automatically extract spatiotemporal features. Then, send the extracted feature vectors to the SoftMax classifier to convert them into action probability distributions and determine the action category. The graph convolution formula of ST-GCN is:

[0038]

[0039] Among them, Z is the feature matrix after convolution; is the normalized adjacency matrix, I is the identity matrix; yes The degree matrix of X is the input feature matrix; Θ is the learnable convolution kernel parameter.

[0040] The formula for spatiotemporal convolution is:

[0041]

[0042] Among them, Z (t) is the output feature of time step t; N(i) is the set of adjacent nodes of node i; C i,k is the normalized coefficient between nodes i and k; X (t-1) is the input feature of time step t-1; Θ is the convolution kernel parameter.

[0043] As a further solution of the present invention, step 5 specifically includes:

[0044] Step 5.1: Select the high-use roads within the hospital and draw a plan map. Select static nodes based on the road capacity and mark them on the road plan map.

[0045] Step 5.2: Identify three types of static nodes in the space outside the hospital: intersections, roadside entrance and exit nodes, and parking lot entrance and exit nodes, and mark them on the road plan;

[0046] Step 5.3: Identify four types of static nodes in the space outside the hospital: outpatient building, inpatient building, guide sign, and roadside parking lot, and mark them on the road plan.

[0047] As a further solution of the present invention, step 6 specifically includes:

[0048] Step 6.1: Determine the location of fixed-point detection based on the maximum depth of field that the ZED 2i depth camera can measure and the length of the road to ensure that the entire road section can be effectively detected. Select a corresponding date and several fixed time points as the starting point, and perform fixed-point recording for a fixed duration in each gathering space. Use people as the detection object, activate the location tracking module, and update the official open source algorithm to obtain and store real-time instantaneous speed data.

[0049] Step 6.2: Calculate the congestion threshold using the proportion of frail patients and wheelchair users. Use the k-means algorithm to find cluster centers through multiple iterations of the resulting sets of instantaneous speed values ​​to determine the congestion status of high-frequency medical treatment routes. The congestion threshold is calculated as follows:

[0050]

[0051] Where ω is the threshold number, the unit is person / m / min; is the proportion of frail patients in the hospital; is the proportion of patients in wheelchairs in the hospital; K is the crowding threshold, in person / m / min; 48 and 33 are the general movement speeds of frail patients and wheelchair patients, in person / m / min, respectively;

[0052] Step 6.3: When a static node is located in a congested road section, the static node is cancelled and the road unit is divided using the static or dynamic node closest to the static node as the start and end points of the road unit;

[0053] When a static node is located in a lightly congested road section, the static node is retained and used as the starting point or end point of the road unit according to the status.

[0054] As a further solution of the present invention, step 7 specifically includes:

[0055] Step 7.1: For the gathering spaces within the hospital, select the congested spaces and generate an area expansion strategy;

[0056] Based on the crowd activity density distribution map of each gathering space, the utilization rate of different locations in the space is obtained. The proportion of crowd activity types is used to understand the traffic demand and temporary rest demand of people in different gathering spaces, and generate strategies for increasing relevant infrastructure in the space.

[0057] Step 7.2: For high-frequency medical treatment roads within the hospital, generate a congestion relief strategy based on the road units divided by dynamic and static nodes;

[0058] Step 7.3: Use the A* intelligent algorithm to obtain the optimal pathfinding route. Draw the planned traffic flow lines, buildings, and green spaces. Define the starting and ending points according to different patient needs. Calculate the f-cost and g-cost to obtain the optimal path.

[0059] The beneficial effects of the present invention are:

[0060] (1) Based on the current status of the external space of the research site and patient-related data, the congestion level, crowd movement speed information, crowd behavior activities, etc. of the established research site are measured with a functional orientation to provide a more scientific and reasonable basis for the optimization and transformation of the site's traffic organization.

[0061] (2) Relying on cutting-edge tools, depth images, point cloud data and intelligent algorithms, a method is proposed to quantitatively analyze the medical efficiency of the space streamline outside the hospital and provide the optimal medical navigation route for patients in the hospital, laying a quantitative data foundation for the precise optimization of the space traffic organization outside the hospital.

[0062] (3) A quantitative analysis and optimization method for the traffic organization outside the hospital environment is proposed for the research site, which is beneficial to promote the cross-development of planning and management of medium and large hospitals in the city, and help related site research to effectively improve medical efficiency and achieve the goal of improving hospital service quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a flowchart of a method for quantitatively analyzing medical streamlines outside a hospital based on depth images and point cloud data provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0065] It is understood that the terms "first," "second," etc., used herein may be used to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script without departing from the scope of this application.

[0066] Figure 1 The flowchart of the method for quantitative analysis of medical streamlines outside the hospital based on depth images and point cloud data provided by the embodiment of the present invention is as follows: Figure 1 As shown, the method includes:

[0067] Step 1: Set up the ZED 2i camera operating environment and test it on the terminal to confirm the environment configuration status;

[0068] Step 2: Obtain real data on outpatient and inpatient visits, and collect and process hospital status data. Through quantitative data analysis and qualitative status research, obtain patient flow at different time periods, the correlation between patient flow and time, and patients' high-frequency medical treatment paths and gathering spaces;

[0069] Step 3: Use the ZED 2i depth camera to detect people in high-frequency medical gathering spaces, obtain the three-dimensional spatial coordinates of each target object in each gathering space, and use the camera to perform plane detection on the space to obtain a grid plane map with coordinate values. Analyze the probability density distribution of people's activities at different locations in the space to represent the utilization rate of different locations in the space. Calculate the area occupied by pedestrians and use different thresholds to represent the congestion status of the gathering space.

[0070] Step 4: Use the ZED 2i depth camera to track people in high-frequency medical gathering spaces, obtain 18 skeletal points, construct a spatiotemporal graph, and pass it to the ST-GCN model. The ST-GCN model extracts the spatiotemporal features of the skeletal sequence, determines the action category, and calculates the proportion of action types in the space.

[0071] Step 5: Use road static information to divide high-frequency medical roads and select static nodes, which include traffic capacity nodes, road condition nodes, and roadside information nodes;

[0072] Step 6: Use the ZED 2i depth camera to detect people on frequently visited medical routes, obtaining information on the movement speed of each person as they pass through each segment of the route. Quantitative data is combined with different thresholds to characterize the congestion status of each road segment. The intersections of sections with different congestion levels are defined as dynamic nodes, which are then integrated with static nodes to form different road units.

[0073] Step 7: Analyze the current status of the qualitative research and quantitative data of high-frequency medical gathering spaces and high-frequency medical roads, derive corresponding strategies for optimizing medical flow lines outside the hospital, and use intelligent algorithms to calculate medical navigation routes.

[0074] Specifically, step 1 specifically includes:

[0075] Step 1.1: Install CUDA and cuDNN, add CUDA system variables, and test by typing nvcc -V and nvcc --version in the run window of your computer to check whether the variables have been added successfully.

[0076] Step 1.2: Install the SDK that matches your computer's performance and CUDA version. Go to the tools subfolder in the ZED SDK and run ZED Explorer.exe and ZED DepthViewer.exe to check whether the ZED camera can be enabled and work properly.

[0077] Step 1.3: Install OpenCV using Anaconda. Navigate to the downloaded file path and enter the installation command. Then install the appropriate opencv-contrib-python and run the get_python_api.py file to complete the ZED_SDK environment setup.

[0078] Step 1.4: Test the installation environment. Create a new file test.py and run it in the terminal. If the terminal displays the camera serial number, it means that the ZED configuration is complete.

[0079] Specifically, step 2 specifically includes:

[0080] Step 2.1: Analyze the current situation of the hospital under study, analyze the traffic flow organization and medical environment, and conduct a qualitative analysis of the current problems;

[0081] Step 2.2: Divide the acquired data of tens of thousands of patients into different time units, and retrieve patient data in different months, weekdays and holidays, and different time periods of the day.

[0082] The correlation between patient flow and different time units is calculated using the corresponding patient flow and time values ​​and their average values, and used as the basis for setting the shooting period. The correlation calculation formula is:

[0083]

[0084] Among them, r is the correlation, f i and t i are the quantitative values ​​of patient flow and time, and is the average value of patient flow and time;

[0085] Step 2.3: Divide the patient data into different treatment departments and inpatient wards. Based on the hospital's department and inpatient building layout, replace the scattered departments and wards in the patient data with different numbers;

[0086] Step 2.4: Use the numbering frequency to determine the spaces and roads where patients frequently seek medical treatment within the hospital, and determine them as the points and routes for photography and data analysis.

[0087] Specifically, step 3 specifically includes:

[0088] Step 3.1: Using the ZED 2i depth camera's object detection function and based on the correlation analysis between crowds and time, select six starting points: 8:00 AM, 10:00 AM, 12:00 PM, 2:00 PM, 4:00 PM, and 6:00 PM from Monday to Wednesday and holidays. Record a 30-minute fixed-point recording of each gathering space, set people as detection targets, activate the location tracking module, and track the crowd.

[0089] The main programming code for the camera to perform target detection is as follows:

[0090]

[0091] Step 3.2: Store and traverse the list of human object spatial coordinates to obtain continuous three-dimensional spatial coordinate points of all human objects, remove object data with a confidence level less than 10%, and retain the frame-by-frame continuous plane spatial coordinates (x, z) of the remaining objects;

[0092] The main programming code for obtaining relevant information about the detection object is as follows:

[0093] object_position=object.position

[0094] ifobject_tracking_state==sl.OBJECT_TRACKING_STATE.OK:

[0095] print("Object{0}is tracked\n".format(object_id))

[0096] Step 3.3: Use the Matplotlib drawing library to read the plane coordinate data of the human object, use kernel density estimation to draw a density scatter plot, and use the ZED 2i depth camera to detect the aggregation space plane. Enable position tracking, obtain the plane data, and convert it into a grid to obtain the plane information of the aggregation space. The kernel density estimation formula is:

[0097]

[0098] in, is the estimated value of the unknown density function f at point x; n is the number of samples; x i is the observed value of the i-th sample; K is the kernel function, a non-decreasing symmetric distribution; h is the smoothing parameter used to control the smoothness of the estimate;

[0099] The main programming code for the plane transformation grid is as follows:

[0100] find_plane_status=zed.find_plane_at_hit(coord,plane)

[0101] mesh = sl.Mesh()

[0102] mesh = plane.extract_mesh()

[0103] Step 3.4: Unify the coordinate systems of the gathering space plan and the scatter density map and overlay them to obtain the crowd activity density distribution of the gathering space. Use the crowd activity density distribution map to represent the utilization rate of different locations in the gathering space.

[0104] Step 3.5: Calculate the pedestrian occupied area of ​​the gathering space based on the planar width of the space and the peak passenger flow obtained from target detection. Based on the "Public Transportation Capacity and Service Quality Manual" and taking into account the structural characteristics of special populations within the hospital, make appropriate adjustments. Calculate the congestion threshold based on the proportion of patients in wheelchairs and the proportion of patients requiring bed propulsion. Use the k-means algorithm to find the cluster center through multiple iterations of the calculated multiple groups of pedestrian occupied area values ​​to determine the congestion status of the gathering space. The pedestrian occupied area calculation formula is:

[0105]

[0106] Where S is the area occupied by pedestrians, in m 2 / person; M is the effective area of ​​the gathering space, unit is m 2 ; Q is the passenger flow during peak hours, the unit is people;

[0107] The calculation formula for the congestion threshold is:

[0108] θ=1.04k1+10k2+(1-k1-k2)C;

[0109] Where θ is the threshold number, the unit is m 2 / person; k1 is the proportion of patients in wheelchairs in the hospital; k2 is the proportion of patients who need beds to move forward in the hospital; C is the threshold for evaluating the degree of crowding in general space, unit is m 2 / person; 1.04 and 10 are the general floor space values ​​for patients in wheelchairs and patients who need to be pushed forward by beds, respectively, in m 2 / people.

[0110] Specifically, step 4 includes:

[0111] Step 4.1: Use the ZED 2i Depth Camera SDK's depth module and position tracking module to select six starting points: 8:00 AM, 10:00 AM, 12:00 PM, 2:00 PM, 4:00 PM, and 6:00 PM from Monday to Wednesday and on holidays. Record each gathering space for 30 minutes at a fixed point. Extract the 3D positions of the skeleton and key points of each object in the video, discard data with a confidence level less than 10%, and save the 2D coordinate data of the human skeleton nodes in each frame.

[0112] The main programming code to enable the camera's human tracking function is as follows:

[0113] zed_error=zed.enable_body_tracking(detection_parameters)

[0114] ifzed_error! =sl.ERROR_CODE.SUCCESS:

[0115] print("enable_body_tracking",zed_error,"\nExitprogram.")

[0116] Step 4.2: Based on the human joint connection information, the joint coordinates of each frame are constructed into a space-time graph G = (V, E), where V represents the set of joint points and E represents the set of edges. In the space-time graph, edges include spatial edges and temporal edges. Spatial edges represent natural connections between joints, and temporal edges represent connections between the same joints in consecutive frames.

[0117] In step 4.3, the human joint coordinates are passed as input to the ST-GCN model. Multiple graph convolution modules are used to automatically extract spatiotemporal features. The extracted feature vectors are then fed into a SoftMax classifier to convert them into action probability distributions and determine the action categories. For the medical cluster space, the action categories are mainly 'Standing' and 'Walking'. The graph convolution formula for ST-GCN is:

[0118]

[0119] Among them, Z is the feature matrix after convolution; is the normalized adjacency matrix, I is the identity matrix; yes The degree matrix of X is the input feature matrix; Θ is the learnable convolution kernel parameter.

[0120] The formula for spatiotemporal convolution is:

[0121]

[0122] Among them, Z (t) is the output feature of time step t; N(i) is the set of adjacent nodes of node i; C i,k is the normalized coefficient between nodes i and k; X (t-1) is the input feature of time step t-1; Θ is the convolution kernel parameter.

[0123] Specifically, step 5 specifically includes:

[0124] Step 5.1. Select the high-use roads within the hospital and draw a plan map. Select static nodes based on the road section's traffic capacity and mark them on the road plan map. On hospital roads, changes in traffic capacity are often caused by changes in road width, and constricted sections often create congestion points. Therefore, static nodes are identified and marked on the road plan map.

[0125] Step 5.2: Identify three types of static nodes in the space outside the hospital: intersections, roadside entrance and exit nodes, and parking lot entrance and exit nodes, and mark them in the road plan;

[0126] Step 5.3: Identify four types of static nodes in the space outside the hospital: outpatient building, inpatient building, guide sign, and roadside parking lot, and mark them on the road plan.

[0127] Specifically, step 6 includes:

[0128] Step 6.1. Determine the location for fixed-point detection based on the maximum depth of field that the ZED 2i depth camera can measure and the length of the road to ensure that the entire road section can be effectively detected. Select six starting times: 8:00, 10:00, 12:00, 14:00, 16:00, and 18:00 from Monday to Wednesday and holidays. Record a 30-minute fixed-point recording of each road section, using people as the detection object. Activate the location tracking module and update the official open-source algorithm to obtain and store real-time instantaneous speed data.

[0129] The main programming code for real-time speed measurement of the camera is as follows:

[0130]

[0131] Step 6.2: Based on the Public Transport Capacity and Service Quality Manual and taking into account the structural characteristics of special populations within the hospital, appropriate adjustments were made. The congestion threshold was calculated based on the proportion of frail patients and wheelchair patients. The k-means algorithm was used to find cluster centers through multiple iterations of the resulting multiple sets of instantaneous speed values ​​to determine the congestion status of high-frequency medical treatment routes. The congestion threshold was calculated as follows:

[0132]

[0133] Where ω is the threshold number, the unit is person / m / min; is the proportion of frail patients in the hospital; is the proportion of patients in wheelchairs in the hospital; K is the crowding threshold, in person / m / min; 48 and 33 are the general movement speeds of frail patients and wheelchair patients, in person / m / min, respectively;

[0134] Step 6.3: When a static node is located in a congested road section, the static node is cancelled and the road unit is divided using the static or dynamic node closest to the static node as the start and end points of the road unit;

[0135] When a static node is located in a lightly congested road section, the static node is retained and used as the starting point or end point of the road unit according to the status.

[0136] By integrating dynamic and static nodes to form road unit divisions, problem analysis is conducted based on different road states, nodes and current situations.

[0137] Specifically, step 7 specifically includes:

[0138] Step 7.1: For the gathering spaces within the hospital, select the congested spaces and generate an area expansion strategy;

[0139] Based on the crowd activity density distribution map of each gathering space, the utilization rate of different locations in the space can be determined. Landscape elements can be added to high-density areas to divert crowds and avoid crowds concentrating in certain areas of the space. The proportion of crowd activity types can be used to understand the traffic needs and temporary rest needs of people in different gathering spaces, and based on this, relevant infrastructure within the space can be added.

[0140] Step 7.2: For high-frequency medical treatment roads within the hospital, generate a congestion relief strategy based on the road units divided by dynamic and static nodes;

[0141] Congested sections of road will be widened, nodes on both sides will be locally renovated, routes will be adjusted and crowds will be diverted to increase the utilization rate of relatively unobstructed sections and reduce the pressure on congested sections. At the same time, public transportation such as shuttle buses can be added within the hospital, and reasonable schedules can be set up to alleviate road congestion.

[0142] Step 7.3: Use the A* intelligent algorithm to obtain the optimal pathfinding route. Draw the planned traffic flow lines, buildings, and green spaces. Define the starting and ending points according to different patient needs. Calculate the f-cost and g-cost to obtain the optimal path.

[0143] The main formulas and programming codes are as follows:

[0144] f(n)=g(n)+h(n)

[0145] Among them, g(n) is the actual cost from the initial node to node n, and h(n) is the estimated cost from node n to the target node.

[0146]

[0147]

[0148] It should be understood that, although the various steps in the flow chart of each embodiment of the present invention are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence according to the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in order, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0149] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link

[0150] (Synchlink) DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct RAMbus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0151] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0152] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the present invention shall be determined by the appended claims.

[0153] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A quantitative analysis method for medical streamlines outside hospitals based on depth images and point cloud data, characterized by: The method comprises: Step 1: Set up the ZED 2i camera operating environment and test it on the terminal to confirm the environment configuration status; Step 2: Obtain real data on outpatient and inpatient visits, and collect and process hospital status data. Through quantitative data analysis and qualitative status research, obtain patient flow at different time periods, the correlation between patient flow and time, and patients' high-frequency medical treatment paths and gathering spaces; Step 3: Use the ZED 2i depth camera to detect people in high-frequency medical gathering spaces, obtain the three-dimensional spatial coordinates of each target object in each gathering space, and use the camera to perform plane detection on the space to obtain a grid plane map with coordinate values. Analyze the probability density distribution of people's activities at different locations in the space to represent the utilization rate of different locations in the space. Calculate the area occupied by pedestrians and use different thresholds to represent the congestion status of the gathering space. Step 4: Use the ZED 2i depth camera to track people in high-frequency medical gathering spaces, obtain 18 skeletal points, construct a spatiotemporal graph, and pass it to the ST-GCN model. The ST-GCN model extracts the spatiotemporal features of the skeletal sequence, determines the action category, and calculates the proportion of action types in the space. Step 5: Use road static information to divide high-frequency medical roads and select static nodes, which include traffic capacity nodes, road condition nodes, and roadside information nodes; Step 6: Use the ZED 2i depth camera to detect people on frequently visited medical routes, obtaining information on the movement speed of each person as they pass through each segment of the route. Quantitative data is combined with different thresholds to characterize the congestion status of each road segment. The intersections of sections with different congestion levels are defined as dynamic nodes, which are then integrated with static nodes to form different road units. Step 7: Analyze the current status of the qualitative research and quantitative data of high-frequency medical gathering spaces and high-frequency medical roads, derive the corresponding strategy for optimizing medical flow lines outside the hospital, and calculate the medical navigation route.

2. The method according to claim 1, characterized in that Step 2 specifically includes: Step 2.1: Analyze the current situation of the hospital under study, analyze the traffic flow organization and medical environment, and conduct a qualitative analysis of the current problems; Step 2.2: Divide the acquired data of tens of thousands of patients into different time units, and retrieve patient data in different months, weekdays and holidays, and different time periods of the day. The correlation between patient flow and different time units is calculated using the corresponding patient flow and time values ​​and their average values, and used as the basis for setting the shooting period. The correlation calculation formula is: Among them, r is the correlation, f i and t i are the quantitative values ​​of patient flow and time, respectively, and is the average value of patient flow and time; Step 2.3: Divide the patient data into different treatment departments and inpatient wards. Based on the hospital's department and inpatient building layout, replace the scattered departments and wards in the patient data with different numbers; Step 2.4: Use the numbering frequency to determine the spaces and roads where patients frequently seek medical treatment within the hospital, and determine them as the points and routes for photography and data analysis.

3. The method according to claim 2, characterized in that Step 3 specifically includes: Step 3.1: Using the ZED 2i depth camera's object detection function, based on the crowd-time correlation analysis results, select a specific date and several fixed time points as the starting point. Record a fixed-length recording of each gathering space, set people as detection targets, activate the location tracking module, and track the crowd. Step 3.2: Store and traverse the list of human object spatial coordinates to obtain continuous three-dimensional spatial coordinate points of all human objects, remove object data with a confidence level less than 10%, and retain the frame-by-frame continuous plane spatial coordinates (x, z) of the remaining objects; Step 3.3: Read the plane coordinate data of the human object, use kernel density estimation to draw a density scatter plot, and use the ZED2i depth camera to detect the aggregation space plane. Enable position tracking, obtain the plane data and convert it into a grid to obtain the plane information of the aggregation space. The kernel density estimation formula is: in, is the estimated value of the unknown density function f at point x; n is the number of samples; x i is the observed value of the i-th sample; K is the kernel function, a non-decreasing symmetric distribution; h is the smoothing parameter used to control the smoothness of the estimate; Step 3.4: Unify the coordinate systems of the gathering space plan and the scatter density map and overlay them to obtain the crowd activity density distribution of the gathering space. Use the crowd activity density distribution map to represent the utilization rate of different locations in the gathering space. Step 3.5: Calculate the pedestrian occupied area of ​​the gathering space based on the planar width of the space and the peak pedestrian flow obtained from target detection. Perform appropriate adjustments based on the structural characteristics of the special population within the hospital. Calculate the congestion threshold using the proportion of patients in wheelchairs and the proportion of patients requiring bed propulsion. Iterate the multiple sets of calculated pedestrian occupied area values ​​to find the cluster center and determine the congestion status of the gathering space. The pedestrian occupied area calculation formula is: Where S is the area occupied by pedestrians, in m 2 / person; M is the effective area of ​​the gathering space, unit is m 2 ; Q is the passenger flow during peak hours, the unit is people; The calculation formula for the congestion threshold is: θ=1.04k1+10k2+(1-k1-k2)C; Where θ is the threshold number, the unit is m 2 / person; k1 is the proportion of patients in wheelchairs in the hospital; k2 is the proportion of patients who need beds to move forward in the hospital; C is the threshold for evaluating the degree of crowding in general space, unit is m 2 / person; 1.04 and 10 are the general floor space values ​​for patients in wheelchairs and patients who need to be pushed forward by beds, respectively, in m 2 / people.

4. The method according to claim 3, characterized in that Step 4 specifically includes: Step 4.1: Using the ZED 2i Depth Camera SDK's depth module and position tracking module, select a date and several fixed time points as starting points, perform fixed-point recording of each gathering space for a fixed duration, extract the 3D positions of the skeleton and key points of each object in the video, remove data with a confidence level less than 10%, and save the 2D coordinate data of the human skeleton nodes in each frame. Step 4.2: Based on the human joint connection information, the joint coordinates of each frame are constructed into a space-time graph G = (V, E), where V represents the set of joint points and E represents the set of edges. In the space-time graph, edges include spatial edges and temporal edges. Spatial edges represent natural connections between joints, and temporal edges represent connections between the same joints in consecutive frames. Step 4.3: Pass the human joint coordinates as input to the ST-GCN model. Use multiple graph convolution modules to automatically extract spatiotemporal features. Then, send the extracted feature vectors to the SoftMax classifier to convert them into action probability distributions and determine the action category. The graph convolution formula of ST-GCN is: Among them, Z is the feature matrix after convolution; is the normalized adjacency matrix, I is the identity matrix; yes The degree matrix of X is the input feature matrix; Θ is the learnable convolution kernel parameter; The formula for spatiotemporal convolution is: Among them, Z (t) is the output feature of time step t; N(i) is the set of adjacent nodes of node i; C i,k is the normalized coefficient between nodes i and k; X (t-1) is the input feature of time step t-1; Θ is the convolution kernel parameter.

5. The method according to claim 4, characterized in that Step 5 specifically includes: Step 5.1: Select the high-use roads within the hospital and draw a plan map. Select static nodes based on the road capacity and mark them on the road plan map. Step 5.2: Identify three types of static nodes in the space outside the hospital: intersections, roadside entrance and exit nodes, and parking lot entrance and exit nodes, and mark them in the road plan; Step 5.3: Identify four types of static nodes in the space outside the hospital: outpatient building, inpatient building, guide sign, and roadside parking lot, and mark them on the road plan.

6. The method according to claim 5, characterized in that Step 6 specifically includes: Step 6.1: Determine the location of the fixed-point detection based on the maximum depth of field that the ZED 2i depth camera can measure and the length of the road to ensure that the entire road section can be effectively detected. Select the corresponding date and several fixed time points as the starting point, and perform fixed-point recording of each gathering space for a fixed length of time. Use people as the detection object and activate the location tracking module; Step 6.2: Calculate the congestion threshold using the proportion of frail patients and wheelchair users. Iterate the multiple sets of instantaneous speed values ​​to find the cluster center and determine the congestion status of high-frequency medical treatment roads. The congestion threshold is calculated as follows: Where ω is the threshold number, in person / m / min; φ1 is the proportion of frail patients in the hospital; φ2 is the proportion of patients in wheelchairs in the hospital; K is the crowding threshold, in person / m / min; 48 and 33 are the general movement speeds of frail patients and wheelchair patients, respectively, in person / m / min; Step 6.3: When a static node is located in a congested road section, the static node is cancelled and the road unit is divided by using the static and dynamic nodes closest to the static node as the start and end points of the road unit respectively; When a static node is located in a lightly congested road section, the static node is retained and used as the starting point or end point of the road unit according to the status.

7. The method according to claim 6, characterized in that Step 7 specifically includes: Step 7.1: For the gathering spaces within the hospital, select the congested spaces and generate an area expansion strategy; Based on the crowd activity density distribution map of each gathering space, the utilization rate of different locations in the space is obtained. The proportion of crowd activity types is used to understand the traffic demand and temporary rest demand of people in different gathering spaces, and generate strategies for increasing relevant infrastructure in the space. Step 7.2: For high-frequency medical treatment roads within the hospital, generate a congestion relief strategy based on the road units divided by dynamic and static nodes; Step 7.3: Calculate the optimal route and draw the planned traffic flow lines, buildings, and green spaces. Define the starting and ending points according to different patient needs, and obtain the optimal path by calculating the f cost and g cost.

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